MySQL is an open-source database management system that uses SQL and was a popular choice for new web applications in the 2000s. By 2006, MySQL had over 8 million installations but received less than 1% of revenue from the $15 billion database market, which was dominated by Oracle, IBM and Microsoft. MySQL shifted to offering more paid support and services to generate more revenue beyond one-time licensing fees.
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
IDERA Live | Working with Complex Data EnvironmentsIDERA Software
You can watch the replay for this IDERA Live webcast, Working with Complex Data Environments, on the IDERA Resource Center, http://ow.ly/RQSF50A4rIr.
Companies are expanding their systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and developing queries against diverse databases in these complex environments. IDERA's Senior Product Manager, Lisa Waugh will discuss the best approach for dealing with the growing challenges of having data reside on different database platforms with Aqua Data Studio.
Speaker: Lisa Waugh is a Senior Product Manager at IDERA Software for the Aqua Data Studio database IDE tool. She has over 15 years of database industry experience, including speaking engagements and presentations on database tools and technologies, and enjoys defining the direction for database development solutions.
The document discusses Data Vault modeling and its components. It describes Data Vault modeling as a hybrid approach between 3rd normal form and star schema modeling that is flexible, scalable, and adaptable. The key components of Data Vault modeling are hubs, links, and satellites. Hubs contain business keys and metadata. Links represent relationships between hubs. Satellites contain descriptive attributes and change tracking metadata. The document provides guidance on building a Data Vault model by identifying hubs, links, and satellites in accordance with reference rules.
The document describes the Data Vault modeling technique which involves storing historical data from multiple sources in a series of normalized tables. It outlines the key components of a Data Vault including hubs, links, and satellites. It then discusses how to implement a Data Vault using an ETL framework, metadata tables, and automation to load the Data Vault from source systems in a standardized, repeatable process.
Database basics for new-ish developers -- All Things Open October 18th 2021Dave Stokes
Do you wonder why it takes your database to find the top five of your fifty six million customers? Do you really have a good idea of what NULL is and how to use it? And why are some database queries so quick and others frustratingly slow? Relational databases have been around for over fifty years and frustrating developers for at least forty nine of those years. This session is an attempt to explain why sometimes the database seems very fast and other times not. You will learn how to set up data (normalization) to avoid redundancies into tables by their function, how to join two tables to combine data, and why Structured Query Language is so very different than most other languages. And you will see how thinking in sets over records can greatly improve your life with a database.
MySQL is an open-source database management system that uses SQL and was a popular choice for new web applications in the 2000s. By 2006, MySQL had over 8 million installations but received less than 1% of revenue from the $15 billion database market, which was dominated by Oracle, IBM and Microsoft. MySQL shifted to offering more paid support and services to generate more revenue beyond one-time licensing fees.
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
IDERA Live | Working with Complex Data EnvironmentsIDERA Software
You can watch the replay for this IDERA Live webcast, Working with Complex Data Environments, on the IDERA Resource Center, http://ow.ly/RQSF50A4rIr.
Companies are expanding their systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and developing queries against diverse databases in these complex environments. IDERA's Senior Product Manager, Lisa Waugh will discuss the best approach for dealing with the growing challenges of having data reside on different database platforms with Aqua Data Studio.
Speaker: Lisa Waugh is a Senior Product Manager at IDERA Software for the Aqua Data Studio database IDE tool. She has over 15 years of database industry experience, including speaking engagements and presentations on database tools and technologies, and enjoys defining the direction for database development solutions.
The document discusses Data Vault modeling and its components. It describes Data Vault modeling as a hybrid approach between 3rd normal form and star schema modeling that is flexible, scalable, and adaptable. The key components of Data Vault modeling are hubs, links, and satellites. Hubs contain business keys and metadata. Links represent relationships between hubs. Satellites contain descriptive attributes and change tracking metadata. The document provides guidance on building a Data Vault model by identifying hubs, links, and satellites in accordance with reference rules.
The document describes the Data Vault modeling technique which involves storing historical data from multiple sources in a series of normalized tables. It outlines the key components of a Data Vault including hubs, links, and satellites. It then discusses how to implement a Data Vault using an ETL framework, metadata tables, and automation to load the Data Vault from source systems in a standardized, repeatable process.
Database basics for new-ish developers -- All Things Open October 18th 2021Dave Stokes
Do you wonder why it takes your database to find the top five of your fifty six million customers? Do you really have a good idea of what NULL is and how to use it? And why are some database queries so quick and others frustratingly slow? Relational databases have been around for over fifty years and frustrating developers for at least forty nine of those years. This session is an attempt to explain why sometimes the database seems very fast and other times not. You will learn how to set up data (normalization) to avoid redundancies into tables by their function, how to join two tables to combine data, and why Structured Query Language is so very different than most other languages. And you will see how thinking in sets over records can greatly improve your life with a database.
Myth Busters II: BI Tools and Data Virtualization are InterchangeableDenodo
Watch Here: https://bit.ly/2NcqU6F
We take on the 2nd myth about data virtualization and it’s one that suggests a BI tool can substitute a data virtualization software.
You might be thinking: If I can have multi-source queries and define a logical model in my reporting tool, why would I need a data virtualization software?
Reporting tools, no doubt important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data.
Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!
Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.
MySQL vs. NoSQL and NewSQL - survey resultsMatthew Aslett
The results of 451 Research's survey into the competitive dynamic between MySQL, NoSQL, and New SQL database technologies.
Further details at: http://blogs.the451group.com/information_management/?p=1740
Big Data: SQL query federation for Hadoop and RDBMS dataCynthia Saracco
Explore query federation capabilities in IBM Big SQL, which enables programmers to transparently join Hadoop data with relational database management (RDBMS) data.
(OTW13) Agile Data Warehousing: Introduction to Data Vault ModelingKent Graziano
This is the presentation I gave at OakTable World 2013 in San Francisco. #OTW13 was held at the Children's Creativity Museum next to the Moscone Convention Center and was in parallel with Oracle OpenWorld 2013.
The session discussed our attempts to be more agile in designing enterprise data warehouses and how the Data Vault Data Modeling technique helps in that approach.
HOW TO SAVE PILEs of $$$BY CREATING THE BEST DATA MODEL THE FIRST TIME (Ksc...Kent Graziano
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk?
In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.
Database@Home - Data Driven : Loading, Indexing, and Searching with Text and ...Tammy Bednar
This session will cover loading large JSON datasets into Oracle Database 19c, indexing the content and providing a RESTful search interface - all using Oracle Cloud features.
Using your DB2 SQL Skills with Hadoop and SparkCynthia Saracco
Learn about Big SQL, IBM's SQL interface for Apache Hadoop based on DB2's query engine. We'll walk through some code example and discuss Spark integration for JDBC data sources (DB2 and Big SQL) using examples from a hands-on lab. Explore benchmark results comparing Big SQL and Spark SQL at 100TB. This presentation was created for the DB2 LUW TRIDEX Users Group meeting in NYC in June 2017.
Big SQL Competitive Summary - Vendor LandscapeNicolas Morales
IBM's Big SQL is their SQL for Hadoop product that allows users to run SQL queries on Hadoop data. It uses the Hive metastore to catalog table definitions and shares data logic with Hive. Big SQL is architected for high performance with a massively parallel processing (MPP) runtime and runs directly on the Hadoop cluster with no proprietary storage formats required. The document compares Big SQL to other SQL on Hadoop solutions and outlines its performance and architectural advantages.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
SQL Analytics provides data visualization and dashboarding capabilities on data lakes to help analysts, sales executives, marketing teams, and finance departments make more data-driven decisions. It allows users to build live visualizations and dashboards from their data, analyze it using SQL queries for reports or alerts, and connect to over 40 different data sources. Key features include a simple query editor, browsing table schemas, autocomplete functions, secure collaboration through shared dashboards and queries, and automation through alerts and parameterized queries and dashboards.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
#dbhouseparty - Graph Technologies - More than just Social (Distancing) NetworksTammy Bednar
This document discusses graph technologies and Oracle's graph offerings. It begins with introductions of the presenters and an overview of the topics to be covered, including what graphs are, typical use cases, Oracle's graph technologies, and graph modeling. Examples of using graphs to analyze fraudulent money transfers and visualizing graphs are provided. The document also compares the PGQL graph query language to SQL and shows how to create and populate a property graph in Oracle. It outlines Oracle's graph features and provides helpful links for more information.
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...Cynthia Saracco
Got Big Data? Then check out what Big SQL can do for you . . . . Learn how IBM's industry-standard SQL interface enables you to leverage your existing SQL skills to query, analyze, and manipulate data managed in an Apache Hadoop environment on cloud or on premise. This quick technical tour is filled with practical examples designed to get you started working with Big SQL in no time. Specifically, you'll learn how to create Big SQL tables over Hadoop data in HDFS, Hive, or HBase; populate Big SQL tables with data from HDFS, a remote file system, or a remote RDBMS; execute simple and complex Big SQL queries; work with non-traditional data formats and more. These charts are for session ALB-3663 at the IBM World of Watson 2016 conference.
Benefits of SQL Server 2017 and 2019 | IDERAIDERA Software
The document discusses several new features introduced in SQL Server 2017 and 2019, including batch mode execution using only one parallelism zone for less processing in 2019; table-valued function interleaved execution running multi-statement functions first in 2017; and adaptive joins expanding to rowstore indexes in 2019. It also mentions adaptive memory grants, scalar function inlining, and automatic tuning as additional new features.
Sergiy Lunyakin "Cloud BI with Azure Analysis Services"DataConf
This document provides an overview of using Azure Analysis Services for cloud business intelligence (BI). It discusses the key components of Azure that work with Analysis Services, including Data Factory, SQL Database, SQL Data Warehouse, and Power BI. It also covers the architecture and performance levels of Analysis Services in Azure, how to connect various data sources, and tools for management, development, and troubleshooting. The document demonstrates how Analysis Services provides a fully managed tabular model engine in the cloud for enterprise-grade data modeling and analytics.
Big Data: InterConnect 2016 Session on Getting Started with Big Data AnalyticsCynthia Saracco
Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Kent Graziano
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
Varadarajan Sourirajan is a data architect with over 16 years of experience seeking a new position. He has extensive experience in data modeling for both online transaction processing and data warehousing applications. Currently he is working on implementing a data warehouse for the treasury line of business at a large bank in the US, drawing on his experience delivering previous data warehouse projects and a proven track record of success.
MySQL 8.0 provides significant performance and functionality enhancements over MySQL 5.7, including 3x better performance, new features like JSON support and window functions, and improved security, replication, and data dictionary capabilities. It underwent 2 years of development with over 5000 bugs fixed and new tests added.
The document summarizes new features and enhancements in MySQL versions 5.7 and 8.0. Key points include:
- MySQL 8.0 provides 3x better performance than 5.7 through optimizations to the optimizer cost model, replication, and other areas.
- New features in 8.0 include JSON support, common table expressions, window functions, and improved security.
- Benchmark results show significant performance improvements for IO-bound and update workloads on MySQL 8.0 compared to 5.7.
The document discusses the state of MySQL in 2019. It notes that MySQL has the second largest market share among databases and is the most used database according to surveys of developers. It outlines many new features introduced in MySQL 8.0 over two years of development, including a document store and improved performance. The role of the community in contributing to MySQL and examples of successful collaboration are also discussed.
MySQL powers the most demanding Web, E-commerce, SaaS and Online Transaction Processing (OLTP) applications.
It is a fully integrated transaction-safe, ACID compliant database with full commit, rollback, crash recovery and row level locking capabilities.
MySQL delivers the ease of use, scalability, and performance to power Facebook, Google, Twitter, Uber, Booking.com and many more...
Myth Busters II: BI Tools and Data Virtualization are InterchangeableDenodo
Watch Here: https://bit.ly/2NcqU6F
We take on the 2nd myth about data virtualization and it’s one that suggests a BI tool can substitute a data virtualization software.
You might be thinking: If I can have multi-source queries and define a logical model in my reporting tool, why would I need a data virtualization software?
Reporting tools, no doubt important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data.
Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!
Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.
MySQL vs. NoSQL and NewSQL - survey resultsMatthew Aslett
The results of 451 Research's survey into the competitive dynamic between MySQL, NoSQL, and New SQL database technologies.
Further details at: http://blogs.the451group.com/information_management/?p=1740
Big Data: SQL query federation for Hadoop and RDBMS dataCynthia Saracco
Explore query federation capabilities in IBM Big SQL, which enables programmers to transparently join Hadoop data with relational database management (RDBMS) data.
(OTW13) Agile Data Warehousing: Introduction to Data Vault ModelingKent Graziano
This is the presentation I gave at OakTable World 2013 in San Francisco. #OTW13 was held at the Children's Creativity Museum next to the Moscone Convention Center and was in parallel with Oracle OpenWorld 2013.
The session discussed our attempts to be more agile in designing enterprise data warehouses and how the Data Vault Data Modeling technique helps in that approach.
HOW TO SAVE PILEs of $$$BY CREATING THE BEST DATA MODEL THE FIRST TIME (Ksc...Kent Graziano
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk?
In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.
Database@Home - Data Driven : Loading, Indexing, and Searching with Text and ...Tammy Bednar
This session will cover loading large JSON datasets into Oracle Database 19c, indexing the content and providing a RESTful search interface - all using Oracle Cloud features.
Using your DB2 SQL Skills with Hadoop and SparkCynthia Saracco
Learn about Big SQL, IBM's SQL interface for Apache Hadoop based on DB2's query engine. We'll walk through some code example and discuss Spark integration for JDBC data sources (DB2 and Big SQL) using examples from a hands-on lab. Explore benchmark results comparing Big SQL and Spark SQL at 100TB. This presentation was created for the DB2 LUW TRIDEX Users Group meeting in NYC in June 2017.
Big SQL Competitive Summary - Vendor LandscapeNicolas Morales
IBM's Big SQL is their SQL for Hadoop product that allows users to run SQL queries on Hadoop data. It uses the Hive metastore to catalog table definitions and shares data logic with Hive. Big SQL is architected for high performance with a massively parallel processing (MPP) runtime and runs directly on the Hadoop cluster with no proprietary storage formats required. The document compares Big SQL to other SQL on Hadoop solutions and outlines its performance and architectural advantages.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
SQL Analytics provides data visualization and dashboarding capabilities on data lakes to help analysts, sales executives, marketing teams, and finance departments make more data-driven decisions. It allows users to build live visualizations and dashboards from their data, analyze it using SQL queries for reports or alerts, and connect to over 40 different data sources. Key features include a simple query editor, browsing table schemas, autocomplete functions, secure collaboration through shared dashboards and queries, and automation through alerts and parameterized queries and dashboards.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
#dbhouseparty - Graph Technologies - More than just Social (Distancing) NetworksTammy Bednar
This document discusses graph technologies and Oracle's graph offerings. It begins with introductions of the presenters and an overview of the topics to be covered, including what graphs are, typical use cases, Oracle's graph technologies, and graph modeling. Examples of using graphs to analyze fraudulent money transfers and visualizing graphs are provided. The document also compares the PGQL graph query language to SQL and shows how to create and populate a property graph in Oracle. It outlines Oracle's graph features and provides helpful links for more information.
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...Cynthia Saracco
Got Big Data? Then check out what Big SQL can do for you . . . . Learn how IBM's industry-standard SQL interface enables you to leverage your existing SQL skills to query, analyze, and manipulate data managed in an Apache Hadoop environment on cloud or on premise. This quick technical tour is filled with practical examples designed to get you started working with Big SQL in no time. Specifically, you'll learn how to create Big SQL tables over Hadoop data in HDFS, Hive, or HBase; populate Big SQL tables with data from HDFS, a remote file system, or a remote RDBMS; execute simple and complex Big SQL queries; work with non-traditional data formats and more. These charts are for session ALB-3663 at the IBM World of Watson 2016 conference.
Benefits of SQL Server 2017 and 2019 | IDERAIDERA Software
The document discusses several new features introduced in SQL Server 2017 and 2019, including batch mode execution using only one parallelism zone for less processing in 2019; table-valued function interleaved execution running multi-statement functions first in 2017; and adaptive joins expanding to rowstore indexes in 2019. It also mentions adaptive memory grants, scalar function inlining, and automatic tuning as additional new features.
Sergiy Lunyakin "Cloud BI with Azure Analysis Services"DataConf
This document provides an overview of using Azure Analysis Services for cloud business intelligence (BI). It discusses the key components of Azure that work with Analysis Services, including Data Factory, SQL Database, SQL Data Warehouse, and Power BI. It also covers the architecture and performance levels of Analysis Services in Azure, how to connect various data sources, and tools for management, development, and troubleshooting. The document demonstrates how Analysis Services provides a fully managed tabular model engine in the cloud for enterprise-grade data modeling and analytics.
Big Data: InterConnect 2016 Session on Getting Started with Big Data AnalyticsCynthia Saracco
Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Kent Graziano
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
Varadarajan Sourirajan is a data architect with over 16 years of experience seeking a new position. He has extensive experience in data modeling for both online transaction processing and data warehousing applications. Currently he is working on implementing a data warehouse for the treasury line of business at a large bank in the US, drawing on his experience delivering previous data warehouse projects and a proven track record of success.
MySQL 8.0 provides significant performance and functionality enhancements over MySQL 5.7, including 3x better performance, new features like JSON support and window functions, and improved security, replication, and data dictionary capabilities. It underwent 2 years of development with over 5000 bugs fixed and new tests added.
The document summarizes new features and enhancements in MySQL versions 5.7 and 8.0. Key points include:
- MySQL 8.0 provides 3x better performance than 5.7 through optimizations to the optimizer cost model, replication, and other areas.
- New features in 8.0 include JSON support, common table expressions, window functions, and improved security.
- Benchmark results show significant performance improvements for IO-bound and update workloads on MySQL 8.0 compared to 5.7.
The document discusses the state of MySQL in 2019. It notes that MySQL has the second largest market share among databases and is the most used database according to surveys of developers. It outlines many new features introduced in MySQL 8.0 over two years of development, including a document store and improved performance. The role of the community in contributing to MySQL and examples of successful collaboration are also discussed.
MySQL powers the most demanding Web, E-commerce, SaaS and Online Transaction Processing (OLTP) applications.
It is a fully integrated transaction-safe, ACID compliant database with full commit, rollback, crash recovery and row level locking capabilities.
MySQL delivers the ease of use, scalability, and performance to power Facebook, Google, Twitter, Uber, Booking.com and many more...
MySQL Day Paris 2018 - What’s New in MySQL 8.0 ?Olivier DASINI
MySQL 8.0 introduces several new features for developers including a document store for working with JSON documents, over 20 new JSON functions, UTF-8 as the default character set, common table expressions (CTEs) for hierarchical data traversal, window functions for analytics, and new options like SKIP LOCKED and NOWAIT for better handling of locked rows. The MySQL Shell provides a way to prototype applications using the new X DevAPI and import JSON data. Many new features in MySQL 8.0 were added to boost developer and data analyst productivity.
MySQL Connector/J in the Making of Modern ApplicationsFilipe Silva
The document discusses MySQL Connector/J and its role in developing modern applications. Connector/J is MySQL's flagship connector for Java that combines the traditional JDBC API and the new X DevAPI. It supports both the MySQL Protocol and X Protocol, implements the X DevAPI, and allows developers to work with JSON documents and relational data in MySQL simultaneously through a fluent API. Connector/J is open source, available via Maven, and the recommended version for new projects.
MySQL InnoDB cluster provides a complete high availability solution for MySQL. MySQL Shell includes AdminAPI which enables you to easily configure and administer a group of at least three MySQL server instances to function as an InnoDB cluster. Each MySQL server instance runs MySQL Group Replication, which provides the mechanism to replicate data within InnoDB clusters, with built-in failover. In the presentation, we will learn on how to set up InnoDB cluster using the official MySQL Docker containers and run them with docker-compose. This presentation covers a demo, including how to connect to the cluster through MySQL Router using a simple application.
Oracle Code Event - MySQL JSON Document StoreMark Swarbrick
The document discusses MySQL 8.0 and its new capabilities as a document store with ACID transactions. Key points include:
- MySQL 8.0 allows storing and querying JSON documents like a NoSQL database while maintaining ACID transactions and the reliability of MySQL.
- This provides the flexibility of a document model with the transactional guarantees of a relational database in a single product.
- The MySQL Shell and X DevAPI connectors allow easy document operations and transactions across languages like JavaScript, Python, Java and C++.
Need to dive into #MySQL suddenly and find out, briefly, what can be done with MySQL technology? NoSQL, MySQL 8.0, Highly Available, InnoDB Cluster & MySQL Cluster both Community & Enterprise Edition. It's all here.
MySQL 8.0 includes several new features and enhancements to improve performance, security, and flexibility for developers. Key updates include support for JSON and Unicode, window functions and common table expressions for data analysis, and security features like SQL roles and dynamic privileges. The new release also aims to make applications more scalable, stable, and mobile-friendly.
MySQL 8.0 includes several new features and enhancements to improve performance, security, and flexibility for developers. Key updates include support for JSON and Unicode, window functions and common table expressions for data analysis, and security features like SQL roles and dynamic privileges. The new release also aims to make applications more scalable, mobile-friendly, and cloud-ready.
MySQL Document Store (Oracle Code Warsaw 2018)Vittorio Cioe
Utilizing MySQL as a document store and storing data in NO SQL fashion it is not only possible, but it also brings the advantages of NO SQL operations together with the power of a relational database. Combining this two aspects it is possible to get fast access to data for applications which want to benefit of the simplicity of NO SQL, but also it is possible to benefit of the granularity of SQL operations for analytics and insights. In the end, using MySQL as a document store, NO SQL take the meaning of Not Only SQL!
The document discusses MySQL high availability options including:
1) Asynchronous and semi-synchronous replication for high availability deployments.
2) MySQL InnoDB Cluster which uses Group Replication, MySQL Router, and MySQL Shell to provide an integrated high availability solution.
3) Examples of deploying MySQL InnoDB Cluster in single and multi-data center configurations for high availability and disaster recovery.
The document discusses Oracle NoSQL Database and its features. It provides an overview of NoSQL databases and data models in Oracle NoSQL including key-value, table, and JSON. It also describes Oracle NoSQL's architecture, which uses automatic data sharding and replication across storage nodes for high availability and scalability. Configuration and usage is simplified with libraries and command line tools.
This document discusses deploying MySQL InnoDB Cluster for high availability. It provides an overview of MySQL InnoDB Cluster and compares it to other MySQL and Oracle high availability solutions. It then covers topics like MySQL InnoDB Cluster architecture, example deployments, configuration settings for replication, failover consistency, network reliability and adding replicas. Finally, it discusses MySQL Router configuration and using MySQL Shell and MySQL Enterprise Backup for management and recovery.
The document discusses new features in MySQL 8.0 including a document store for JSON documents, common table expressions and window functions, improved performance, replication enhancements, and role-based access control. It provides examples of how MySQL 8.0 offers both SQL and NoSQL capabilities through the addition of a document store and improved JSON functions and performance.
MySQL 20 años: pasado, presente y futuro; conoce las nuevas características d...GeneXus
The document is a safe harbor statement outlining Oracle's general product direction and disclaiming any commitments. It states that the information is intended for informational purposes only and should not be relied upon for purchasing decisions. It also notes that Oracle has sole discretion over releasing any product features or functionality mentioned. The document is copyrighted by Oracle in 2015.
An outline on why the MySQL 8 release is viewed as a gamechanger with a look at some of the new features like CTEs, Window Functions, MySQL InnoDB Cluster, Enterprise Data Masking, and more
Marc Sewtz, a senior software development manager at Oracle, gave a presentation on Oracle APEX and the Autonomous Database at a Boston meetup in September 2019. He discussed Oracle's new free tier offering for the Autonomous Database and APEX, as well as upcoming features in APEX 19.2 like enhanced list of values, faceted search, and issues management. The presentation provided an overview of Oracle APEX and highlighted new capabilities and services available in both APEX and Oracle Cloud.
How-to-Choose-the-Right-Database-to-Build-High-Performance-Internet-Scale-App...Amazon Web Services
AWS offers the broadest range of purpose-built databases for specific application use cases. In this session, we will show you an overview of the database offering from AWS and elaborate more on Amazon Aurora which is a MySQL- and PostgreSQL-compatible relational database with the speed, reliability, and availability of commercial databases at one-tenth the cost. We will also explore recently released Aurora features, such as Serverless, Multi-Master, and Global databases, and helps you get started.
Similar to MySQL 8.0 Introduction to NoSQL + SQL (20)
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.